Embracing the 3-Month Rule: A Pragmatic Approach to Developing Unscalable Solutions
In the startup realm, one piece of advice stands out: “Do things that don’t scale,” popularized by entrepreneur Paul Graham. While many acknowledge this wisdom, the practical application in software development is often overlooked. Over the past eight months, while working on my AI podcast platform, I’ve devised a straightforward yet effective framework that I call the 3-Month Rule: any unscalable solution is given a trial period of three months. At the conclusion of this period, the solution must either demonstrate its worth and be refined into a robust system, or it will be discarded.
As software engineers, we are often inclined to design scalable architectures from the outset, focusing on complex patterns like microservices and distributed systems to accommodate potentially millions of users. However, such an approach can lead to unnecessary complications, particularly for startups. In my experience, writing scalable code from the beginning can feel like a costly delay, optimizing for users that may never materialize and addressing challenges that might not exist. The 3-Month Rule encourages me to develop straightforward, even “poorly-designed,” code that can be deployed quickly, allowing me to fundamentally understand user needs.
My Current Simplified Practices and Their Insights
1. Centralizing Everything on a Single Virtual Machine
I currently operate my entire platform—database, web server, background jobs, and caching—on a single $40/month virtual machine. This setup prioritizes simplicity over redundancy, with manual local backups.
This approach has unveiled critically important data regarding my resource consumption within just two months. For instance, I discovered that my platform, which I initially anticipated would be “AI-intensive,” typically utilizes only 4GB of RAM during peak times. The intricate Kubernetes configuration I was considering would have mostly involved managing idle containers.
When system failures occur (and they have happened twice), I gain invaluable insights into the actual pain points—often not the areas I initially expected.
2. Utilizing Hardcoded Configuration Values
Instead of employing configuration files or environment variables, I use hardcoded values across my codebase:
plaintext
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
While this practice may seem counterintuitive, it has proven very effective. With just a simple search, I can locate any configuration
